Mining mobile trajectories

Smyth, Carl Stephen

doi:10.4324/9780203468029_chapter_14

Cited by 21 publications

(20 citation statements)

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“…Non-stop generation of space-time trajectories from different kinds of moving entities provides the possibility to discover useful and interesting information about movement of human, animals, vehicles, to find patterns, extract their meaning, and expand our knowledge about the mobile world [26]. A review of the literature on moving object data mining and visualization highlights the importance and significant progress in this area.…”

Section: Moving Object Data Miningmentioning

confidence: 99%

Exploring movement-similarity analysis of moving objects

2009

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Extracting knowledge about the movement of different types of mobile agents (e.g. human, animals, vehicles) and dynamic phenomena (e.g. hurricanes) requires new exploratory data analysis methods for massive movement datasets. Different types of moving objects share similarities but also express differences in terms of their dynamic behavior and the nature of their movement. Extracting such similarities can significantly contribute to the prediction, modeling and simulation dynamic phenomena. Therefore, with the development of a quantitative methodology this research intends to investigate and explore similarities in the dynamics of moving objects by using methods of GIScience in knowledge discovery. This paper presents a summary of the ongoing Ph.D. research project.

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Section: Moving Object Data Miningmentioning

confidence: 99%

Exploring movement-similarity analysis of moving objects

2009

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“…Data mining applications can be found in [5,6]. The most fundamental works in these contexts is on similarity measuring and indexing, e.g.…”

Section: Related Workmentioning

confidence: 99%

“…Data management and data mining on trajectory data had been studied in many applications [1,2,3,4,5]. Data mining applications can be found in [5,6].…”

Section: Related Workmentioning

confidence: 99%

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Symbolization of Mobile Object Trajectories with the Support to Motion Data Mining

Jin

Wang

Sun

2004

Lecture Notes in Computer Science

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Abstract. Extraction and representation of the events in trajectory data enable us go beyond the primitive and quantitative values and focus on the high level knowledge. On the other hand, it enables the applications of vast off the shelf methods, which was originally designed for mining event sequences, to trajectory data. In this paper, the problem of symbolizing trajectory data is addressed. We first introduce a static symbolization method, in which typical subtrajectories are generated automatically based on the data. For facilitating the data mining process on streaming trajectories, we also present an incremental method, which dynamically adjusts the typical sub-trajectories according to the most recent data characters. The performances of our approaches were evaluated on both real data and synthetic data. Experimental results justify the effectiveness of the proposed methods and the superiority of the incremental approach.

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“…Dykes and Mountain search episodes expressing distinctive characteristics of movement, including absolute speed, direction, sinuosity and measurements of their variations [13]. Smyth presents a data mining algorithm that assigns predefined activities to segments of trajectories by analysing some measurable motion descriptors, such as speed, heading and acceleration [57].…”

Section: Limiting Databasesmentioning

confidence: 99%

Reporting Leaders and Followers among Trajectories of Moving Point Objects

et al. 2007

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Abstract. Widespread availability of location aware devices (such as GPS receivers) promotes capture of detailed movement trajectories of people, animals, vehicles and other moving objects, opening new options for a better understanding of the processes involved. In this paper we investigate spatio-temporal movement patterns in large tracking data sets. We present a natural definition of the pattern 'one object is leading others', which is based on behavioural patterns discussed in the behavioural ecology literature. Such leadership patterns can be characterised by a minimum time length for which they have to exist and by a minimum number of entities involved in the pattern. Furthermore, we distinguish two models (discrete and continuous) of the time axis for which patterns can start and end. For all variants of these leadership patterns, we describe algorithms for their detection, given the trajectories of a group of moving entities. A theoretical analysis as well as experiments show that these algorithms efficiently report leadership patterns.

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Mining mobile trajectories

Cited by 21 publications

References 0 publications

Exploring movement-similarity analysis of moving objects

Exploring movement-similarity analysis of moving objects

Symbolization of Mobile Object Trajectories with the Support to Motion Data Mining

Reporting Leaders and Followers among Trajectories of Moving Point Objects

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